Red but not dead: unveiling the star-forming far-infrared spectral energy distribution of SpARCS brightest cluster galaxies at 0 < z < 1.8
Bibliographic record
Abstract
We present the results of a Spitzer/Herschel infrared photometric analysis of the largest (716) and the highest-redshift (z = 1.8) sample of brightest cluster galaxies (BCGs), those from the Spitzer Adaptation of the Red-Sequence Cluster Survey Given the tension that exists between model predictions and recent observations of BCGs at z < 2, we aim to uncover the dominant physical mechanism(s) guiding the stellar mass buildup of this special class of galaxies, the most massive in the Universe and uniquely residing at the centres of galaxy clusters. Through a comparison of their stacked, broad-band, infrared spectral energy distributions (SEDs) to a variety of model templates in the literature, we identify the major sources of their infrared energy output, in multiple redshift bins between 0 < z < 1.8. We derive estimates of various BCG physical parameters from the stacked νLν SEDs, from which we infer a star-forming, as opposed to a ‘red and dead’ population of galaxies, producing tens to hundreds of solar masses per year down to z = 0.5. This discovery challenges the accepted belief that BCGs should only passively evolve through a series of gas-poor, minor mergers since z ∼ 4, but agrees with an improved semi-analytic model of hierarchical structure formation that predicts star-forming BCGs throughout the epoch considered. We attribute the star formation inferred from the stacked infrared SEDs to both major and minor ‘wet’ (gas-rich) mergers, based on a lack of key signatures (to date) of cooling-flow-induced star formation, as well as a number of observational and simulation-based studies that support this scenario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".